A line graph is the visual representation of two sets of quantitative data where one data is dependent on the other. The line graph comprises one horizontal axis denoted as the x-axis and one vertical axis denoted as the y-axis. The x-axis is also called the independent axis because the values on this line do not depend on anything. The y-axis is called the dependent axis because it has values that are dependent on values on the x-axis. The purpose of a line graph is to find out the relationship between two sets of data values.

**Making the Line Graph**

For each value of one set of data, there is a corresponding value of other sets of data, as collected through observation or records. These values are plotted as points and when these points are connected we get the graph that gives a visual indication of how one set of data changes according to the change of another set of data.

For example, the numbers of Covid-19 cases are recorded for each day of a month and can be plotted on a line graph where the date is put along the x-axis and the corresponding Covid-19 cases are plotted along the y-axis. After plotting the data points and connecting through a line, a graph is obtained that gives the information about the changes in the number of Covid-19 cases as per the different days of the month.

**Different Parts of a Line Graph**

- The Title: This gives a short description of the two types of data that is being plotted.
- The Legend: It shows what data is represented by the x-axis and y-axis.
- The Data: This represents the set of data values for both types.
- X-Axis: Indicates the axis of independent data.
- Y-Axis: Indicates the axis for dependent data.

Line graphs are best suited to track changes in data values over time. Line graphs also help to compare changes in different sets of data over the same period. Line graphs provide a simple and quick way to analyze data. It gives a clear indication of the range, the minimum and maximum, as well as the increasing or decreasing trend of values.

**Scatter Plot**

A scatter plot is the plotting of data values of two independent variables in form of dots to observe whether any relationship exists between the two. It contains a horizontal and a vertical axis that indicates values for each data point of two sets of variables. In a scatter plot, the relationship between two variables is called correlation. To find out any predictive correlation between variables through a scatter plot, a trend line is drawn such that it is as close as possible to all points plotted. It is a straight line that mathematically indicates the best fit to the data values. Based on the nature of this trend line, we can analyze what is the relationship between the two variables.

**The Correlation Pattern**

The trend line indicates that the scatter plots of data values can have three different types of correlation as explained below.

**Positive correlation:**

A scatter plot that shows increasing values of both variables means a trend line that slants upwards from left to right.

**Negative correlation: **

A scatter plot that indicates increasing values of the variable along the horizontal axis and corresponding decreasing values for the other variable along the vertical axis. This gives a trend line that slants downward from left to right.

**No Correlation:**

A scatter plot that doesn’t express any increasing or decreasing trend between the values of the variables. In this case, the points are found randomly distributed across the plot.

The analysis of different levels of correlation between the data values is useful to understand their relationship and can be useful for further prediction of data values. You can explore more about this topic on the Cuemath website.